Colon cancer, the second leading cause of cancer deaths for men and women in the United States, can be prevented if precursor colonic polyps are detected and removed. The long-term goal of the proposed project is to advance the early detection of colon cancer. Computed tomographic colonography (CTC) has been proposed as a promising technique for colon cancer screening, but for CTC to be a practical screening tool, many images must be interpreted rapidly and accurately. To this end, the short-term goal of the project is to develop a high-performance computer-aided diagnosis (CAD) scheme for the automated detection of polyps in CTC to assist radiologists in detecting polyps quickly and accurately, by providing them with a "second opinion" regarding the locations of suspicious polyps. We hypothesize that a CAD scheme can significantly reduce radiologists' interpretation time and improve their performance in detecting colonic polyps in CTC. To explore this hypothesis, we propose the following specific aims: Specific Aim 1. Establish a large CTC database of polyps to develop and evaluate a CAD scheme: (1) Collect new CTC cases of polyps retrospectively and prospectively. Specific Aim 2. Develop methods for the detection of polyp candidates: (1) Generate isotropic volumetric data from axial CT images; (2) Develop methods for automated extraction of the colon based on apriori knowledge of the abdominal anatomy; (3) Develop methods for extraction of polyp candidates based on geometric features. Specific Aim 3. Develop methods for the reduction of false positives: (1) Develop methods of clustering of polyp candidates to merge polyp candidates and to remove false positives due to noise; (2) Develop methods based on 3-dimensional volumetric features that differentiate polyps from false positives due to normal anatomic structures; (3) Use diseriminant analysis, artificial neural networks, and genetic algorithms to merge volumetric features for the reduction of false positives. Specific Aim 4. Evaluate the performance and benefit of the overall CAD scheme: (1) Evaluate the performance of the CAD scheme in the detection of polyps in CTC. (2) Evaluate the benefit of the CAD scheme in reducing radiologists' interpretation time and improving diagnostic performance in the detection of polyps by means of an observer study. Successful development of such a CAD scheme will advance the clinical implementation of CT-based colon cancer screening, promote early diagnosis of colon cancer, and ultimately reduce mortality due to colon cancer.